🚀 Beyond Basic RAG
While basic RAG works for simple Q&A, production systems need sophisticated patterns to handle complex queries, large documents, and high accuracy requirements.
Production Reality: Basic RAG achieves ~70% accuracy. Advanced patterns reach 85-95% by solving context fragmentation, query ambiguity, and retrieval-generation misalignment.
🏗️ Advanced RAG Patterns
Hierarchical RAG
Multi-level document structure with parent-child relationships
Chunking
Document → Sections → Paragraphs → Sentences
Retrieval
Query at multiple levels, combine results
Fusion
Hierarchical result fusion with context preservation
✅ Benefits
- • Better context preservation
- • Reduced hallucinations
- • Improved accuracy
⚠️ Challenges
- • Complex indexing
- • Higher latency
- • Storage overhead
Best for: Large documents, legal contracts, technical manuals
Implementation Example
# Hierarchical RAG Implementation
class HierarchicalRAG:
def __init__(self):
self.doc_index = {} # Document level
self.section_index = {} # Section level
self.chunk_index = {} # Chunk level
def index_document(self, doc):
# Create hierarchical structure
sections = self.extract_sections(doc)
for section in sections:
chunks = self.chunk_section(section)
for chunk in chunks:
self.store_with_hierarchy(doc, section, chunk)
def retrieve(self, query, k=5):
# Multi-level retrieval
doc_results = self.search_documents(query)
section_results = self.search_sections(query)
chunk_results = self.search_chunks(query)
# Fuse results with hierarchy context
return self.hierarchical_fusion(
doc_results, section_results, chunk_results
)⚡ RAG Optimization Techniques
Semantic Chunking
Split based on semantic boundaries, not fixed sizes
from sentence_transformers import SentenceTransformer
import numpy as np
def semantic_chunking(text, threshold=0.5):
sentences = sent_tokenize(text)
embeddings = model.encode(sentences)
chunks = []
current_chunk = [sentences[0]]
for i in range(1, len(sentences)):
similarity = cosine_similarity(
embeddings[i-1:i], embeddings[i:i+1]
)[0][0]
if similarity < threshold:
# Topic shift detected
chunks.append(' '.join(current_chunk))
current_chunk = [sentences[i]]
else:
current_chunk.append(sentences[i])
chunks.append(' '.join(current_chunk))
return chunksOverlapping Windows
Create overlapping chunks to preserve boundary context
def overlapping_chunks(text, chunk_size=1000, overlap=200):
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunk = text[start:end]
# Ensure we don't cut words
if end < len(text):
last_space = chunk.rfind(' ')
if last_space > -1:
chunk = chunk[:last_space]
end = start + last_space
chunks.append(chunk)
start = end - overlap
return chunksHierarchical Chunking
Multi-level chunks from document structure
def hierarchical_chunks(document):
hierarchy = {
'document': document.title,
'sections': [],
'paragraphs': [],
'sentences': []
}
for section in document.sections:
hierarchy['sections'].append({
'content': section.content,
'parent': document.title
})
for para in section.paragraphs:
hierarchy['paragraphs'].append({
'content': para.content,
'parent': section.title
})
return hierarchy🧪 RAG Pattern Simulator
📊 Performance Impact
Metrics Comparison
Implementation Priority
🎯 Key Takeaways
Pattern Selection Matters: Choose RAG patterns based on document complexity, query types, and accuracy requirements
Optimize Incrementally: Start with semantic chunking, add fusion, then scale to hierarchical approaches
Measure Everything: Track retrieval accuracy, answer relevance, and latency to guide optimization
Context is King: Advanced RAG patterns excel at preserving and utilizing document context
Production Readiness: Adaptive RAG with self-correction is the gold standard for enterprise systems